Machine Learning Engineer

Job description

**Machine Learning Engineer** **Washington, DC (Hybrid)** **About the Role:** We are seeking a highly skilled Machine Learning Engineer to join our core AI team. In this role, you will focus on deploying, maintaining, and monitoring the AI/ML systems that power our platform. You will work closely with data scientists, data engineers, and product teams to ensure scalable, reliable, and production-grade AI solutions. You’ll play a critical role in operationalizing large language models (LLMs) and other ML systems, ensuring they run efficiently, securely, and with robust monitoring in place. **Key Responsibilities:** - Design, implement, and maintain ML deployment pipelines for scalable production systems. - Operationalize large language models (LLMs) and other AI/ML models, ensuring high availability and reliability. - Build robust model monitoring, logging, and alerting systems to track performance and detect drift. - Partner with data scientists to transition models from research/prototype into production-ready deployments. - Develop CI/CD pipelines for ML workflows, integrating testing, validation, and automated deployment. - Optimize runtime performance of ML models across cloud platforms (AWS, GCP, Azure) and distributed systems. - Apply containerization and orchestration (Docker, Kubernetes) to enable reproducible, scalable systems. - Collaborate with cross-functional teams to ensure ML systems align with platform goals and business requirements. **Qualifications:** - 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or similar role. - Proven experience deploying and maintaining machine learning models in production at scale. - Hands-on experience with ML lifecycle tooling (MLflow, Kubeflow, SageMaker, Vertex AI, or similar). - Strong proficiency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow. - Deep knowledge of containerization (Docker) and orchestration (Kubernetes) for production ML systems. - Expertise with cloud platforms (AWS, GCP, Azure) for ML deployment and scaling. - Strong understanding of MLOps best practices, monitoring, and automation. - Excellent problem-solving skills, with an emphasis on building reliable, scalable systems. - Strong communication and collaboration skills across technical and non-technical teams.